Letter Pair Similarity Classification and URL Ranking Based on Feedback Approach
Search engine is one of the most important applications in today’s internet. For an ambiguous query/topic, different users may have different search goals, so the search engine doesn’t satisfy user information needs properly on the diverse aspects upon submission of same query/topic. The examination of user search goals can be very valuable in improving search engine importance and user knowledge. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users. Here propose a framework that enables large-scale evaluation of personalized search. User interest is employed in the clustering process to achieve personalization effect. The goal of personalized IR (information retrieval) is to return search results that better match the user intent. Then these user search goals are used to restructure and reordered the web search results by using URL ranking process and search history process.
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